CN109814651B - Particle swarm-based photovoltaic cell multi-peak maximum power tracking method and system - Google Patents

Particle swarm-based photovoltaic cell multi-peak maximum power tracking method and system Download PDF

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CN109814651B
CN109814651B CN201910053951.8A CN201910053951A CN109814651B CN 109814651 B CN109814651 B CN 109814651B CN 201910053951 A CN201910053951 A CN 201910053951A CN 109814651 B CN109814651 B CN 109814651B
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particle
optimal position
fitness value
photovoltaic cell
maximum power
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谭智力
杨胜胜
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China University of Geosciences
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Abstract

The invention discloses a particle swarm-based photovoltaic cell multi-peak maximum power tracking method and a particle swarm-based photovoltaic cell multi-peak maximum power tracking system. Compared with the traditional maximum power tracking method, the maximum power tracking method disclosed by the invention can avoid the situation that the search is trapped in local optimum, ensure the tracking precision and speed, reduce the oscillation loss in the tracking process and better accord with the reality.

Description

Particle swarm-based photovoltaic cell multi-peak maximum power tracking method and system
Technical Field
The invention relates to a particle swarm-based photovoltaic cell multi-peak maximum power tracking method and system, and belongs to the field of photovoltaic power generation engineering.
Background
With the gradual consumption of ore energy and the gradual increase of environmental pollution, renewable energy is more and more favored by people. As an environmentally friendly and renewable energy source, a solar photovoltaic power generation technology has been widely used, and Photovoltaic (PV) devices are increasingly put into various applications. However, the problem of photoelectric conversion efficiency has been a technical bottleneck for the development of photovoltaic power generation. In order to develop and utilize solar energy resources to a greater extent, tracking the maximum power point of a photovoltaic power generation system is a very effective way to improve the photoelectric conversion efficiency, however, the main challenge of maximum power tracking is to deal with its non-linear output characteristic varying with temperature and sunlight, and if the whole array is not uniformly illuminated, such as in a city of distributed photovoltaic power generation, the characteristic becomes more complicated and appears as a multi-peak characteristic when encountering the occlusion of trees, the variation of cloud layers and the non-uniform accumulation of dust. The presence of multiple peaks reduces the effectiveness of existing Maximum Power Point Tracking (MPPT) schemes because they cannot distinguish between local peaks and global peaks.
Therefore, the research on the maximum power point tracking of the photovoltaic system under the condition of partial shading is very important. Most of the existing traditional technologies are single-peak maximum power tracking methods, but the traditional methods are slow in tracking speed, easy to generate power oscillation in a steady state and increase power loss, and more importantly, misjudgment is easy to fall into local optimum.
Based on the analysis, the invention provides a maximum power tracking method based on a particle swarm algorithm, which can effectively avoid trapping in local optimization to obtain the maximum power point of a photovoltaic cell group under the condition of local shadow.
Disclosure of Invention
The invention aims to solve the technical problems that most of the prior art is a single-peak maximum power tracking method, and the traditional method has low tracking speed, is easy to generate power oscillation in a steady state, increases power loss and is easy to cause the technical defects that misjudgment is easy to fall into local optimum.
The invention solves the technical problem, and the adopted particle swarm-based photovoltaic cell multi-peak maximum power tracking method comprises the following steps:
s1, open circuit voltage U according to photovoltaic cell arrayocIn [0, U ]oc]M points are selected as the initial voltage V of the particle ii(k)，i∈[1,2,3,…,m]K represents the number of iterations, k is 0 at the initial voltage, and m is a positive integer greater than 1; initializing parameters of the particle population, including values of the particle weight omega and a self-learning factor c1Social learning factor c2The setting range of (1);
s2, calculating the fitness value of each particle, wherein the fitness value is equal to the output power of the photovoltaic cell array; searching an individual optimal position and a global optimal position of the population according to the fitness value of each particle;
s3, updating the position and the speed of each particle to obtain a new fitness value of the next generation particle population;
s4, respectively comparing the individual optimal position and the global optimal position of the next generation particle population with the individual optimal position and the global optimal position before the update so as to respectively update the individual optimal position and the global optimal position, wherein the updating criterion is as follows: after calculating the fitness value of the current position, comparing the fitness value with the optimal fitness value before updating, taking the position corresponding to the larger fitness value as a new individual optimal position of the particle, and taking the position corresponding to the maximum fitness value in all the new individual optimal position particles as a global optimal position;
and S5, returning to the step S3 until a convergence condition is reached or the maximum iteration number is reached, and taking the final global optimal position as the working voltage corresponding to the maximum power point of the photovoltaic system.
Further, in the multi-peak maximum power tracking method of the particle swarm-based photovoltaic cell of the invention, the peak power is [0, U ]oc]The requirement for the selection of the m points is a linear uniform selection or a uniform distribution of the particles at a theoretical value equal to 0.8Uoc。
Further, in the particle swarm-based photovoltaic cell multi-peak maximum power tracking method, the learning factor c is1And c2Is (0,2), learning factor c1And c2Randomly taking values in the interval of (0, 2).
Further, in the particle swarm-based photovoltaic cell multi-peak maximum power tracking method of the present invention, the value of the particle weight ω is a linear decreasing weight, and the calculation formula is:
Figure BDA0001951794510000021
where c is the adjustment coefficient between 0 and 1, k is the number of current iterations, maxgen is the maximum number of iterations, ωminIs the minimum inertial weight, ωmaxIs the maximum inertial weight, fi kIs the fitness value of the ith particle in the kth generation,
Figure BDA0001951794510000022
and
Figure BDA0001951794510000023
respectively the minimum and maximum fitness value of the ith particle in the kth generation,
Figure BDA0001951794510000024
is the weight of the ith particle in the kth generation.
Further, in the multi-peak maximum power tracking method for a photovoltaic cell based on particle swarm of the present invention, the updating of the position and the speed of each particle specifically means:
in the n-dimensional search space, a population consisting of m particles is denoted as X ═ X (X)1,...,xi，...,xm) The position of the ith particle is xi＝(xi1，xi2，...xin)TVelocity vi＝(vi1，vi2，...vin)TThe self-optimum position P searched by the ith particle in the space search processi＝(pi1，pi2，...pid)TGlobal optimum position Pg＝(pg1，pg2，...pgd)TEach particle represents a solution, and the position and velocity of the particle are updated in an iterative manner, according to the formula:
Figure BDA0001951794510000031
Figure BDA0001951794510000032
is the velocity of particle i at the kth iteration, is
Figure BDA0001951794510000033
A velocity component of dimension d;
Figure BDA0001951794510000034
is the position of the particle i at the kth time,
Figure BDA0001951794510000035
is that
Figure BDA0001951794510000036
A position component of d-th dimension;
Figure BDA0001951794510000037
is the individual optimal position of the particle i at the kth iteration,
Figure BDA0001951794510000038
is that
Figure BDA0001951794510000039
An individual optimal position component of the d-th dimension;
Figure BDA00019517945100000310
is the global optimum position of the particle population at the kth iteration,
Figure BDA00019517945100000311
is that
Figure BDA00019517945100000312
The optimal position component in the d-th dimension,
Figure BDA00019517945100000313
is the weight of the ith particle in the kth generation, r1And r2Is at [0,1 ]]Random numbers are uniformly distributed in the interval.
Further, in the multi-peak maximum power tracking method for photovoltaic cells based on particle swarm of the present invention, the reaching of the convergence condition means:
and judging the difference value between the maximum and minimum fitness values corresponding to all the particles, if the difference value is smaller than a preset difference value, indicating that a convergence condition is reached, and finishing the execution of the particle swarm algorithm, otherwise, not reaching the convergence condition.
Further, in the particle swarm-based photovoltaic cell multi-peak maximum power tracking method of the invention, the calculation formula of the fitness value is as follows:
Figure BDA00019517945100000314
Figure BDA00019517945100000315
the speed of the particle i in the k iteration represents the voltage value output by the photovoltaic cell;
Figure BDA00019517945100000316
representing the current measured by the current output voltage of the photovoltaic cell;
Figure BDA00019517945100000317
the objective function value of the particles represents the output power corresponding to the current output voltage of the photovoltaic cell, namely the fitness value.
In order to solve the technical problem, the invention provides a photovoltaic cell multi-peak maximum power tracking system based on particle swarm, which comprises:
an initialization module for the open circuit voltage U according to the photovoltaic cell arrayocIn [0, U ]oc]M points are selected as the initial voltage V of the particle ii(k)，i∈[1,2,3,…,m]K represents the number of iterations, k is 0 at the initial voltage, and m is a positive integer greater than 1; initializing parameters of the particle population, including values of the particle weight omega and a self-learning factor c1Social learning factor c2The setting range of (1);
the optimal position calculation module is used for calculating the fitness value of each particle, and the fitness value is equal to the output power of the photovoltaic cell array; searching an individual optimal position and a global optimal position of the population according to the fitness value of each particle;
the particle population updating module is used for updating the position and the speed of each particle to obtain a new fitness value of the next generation particle population;
an optimal position updating module for respectively comparing the individual optimal position and the global optimal position of the next generation particle population with the individual optimal position and the global optimal position before the update so as to respectively update the individual optimal position and the global optimal position, and the updating criterion is as follows: after calculating the fitness value of the current position, comparing the fitness value with the optimal fitness value before updating, taking the position corresponding to the larger fitness value as a new individual optimal position of the particle, and taking the position corresponding to the maximum fitness value in all the new individual optimal position particles as a global optimal position;
and the optimal position updating module is used for returning to the particle population updating module until a convergence condition is reached or the maximum iteration number is reached, and taking the final global optimal position as the working voltage corresponding to the maximum power point of the photovoltaic system.
Further, in the particle swarm-based photovoltaic cell multi-peak maximum power tracking system of the present invention, the value of the particle weight ω is a linear decreasing weight, and the calculation formula is:
Figure BDA0001951794510000041
where c is the adjustment coefficient between 0 and 1, k is the number of current iterations, maxgen is the maximum number of iterations, ωminIs the minimum inertial weight, ωmaxIs the maximum inertial weight, fi kIs the fitness value of the ith particle in the kth generation,
Figure BDA0001951794510000042
and
Figure BDA0001951794510000043
respectively the minimum and maximum fitness value of the ith particle in the kth generation,
Figure BDA0001951794510000044
is the weight of the ith particle in the kth generation.
Further, in the particle swarm-based photovoltaic cell multi-peak maximum power tracking system of the present invention, the updating of the position and the speed of each particle specifically means:
in the n-dimensional search space, a population consisting of m particles is denoted as X ═ X (X)1,...,xi，...,xm) The position of the ith particle is xi＝(xi1，xi2，...xin)TVelocity vi＝(vi1，vi2，...vin)TThe self-optimum position P searched by the ith particle in the space search processi＝(pi1，pi2，...pid)TGlobal optimum position Pg＝(pg1，pg2，...pgd)TEach particle representing a solutionThe position and velocity of the particle are updated in an iterative manner, with the formula:
Figure BDA0001951794510000045
Figure BDA0001951794510000046
is the velocity of particle i at the kth iteration, is
Figure BDA0001951794510000047
A velocity component of dimension d;
Figure BDA0001951794510000048
is the position of the particle i at the kth time,
Figure BDA0001951794510000049
is that
Figure BDA00019517945100000410
A position component of d-th dimension;
Figure BDA00019517945100000411
is the individual optimal position of the particle i at the kth iteration,
Figure BDA00019517945100000412
is that
Figure BDA00019517945100000413
An individual optimal position component of the d-th dimension;
Figure BDA00019517945100000414
is the global optimum position of the particle population at the kth iteration,
Figure BDA00019517945100000415
is that
Figure BDA00019517945100000416
The optimal position component in the d-th dimension,
Figure BDA00019517945100000417
is the weight of the ith particle in the kth generation, r1And r2Is at [0,1 ]]Random numbers are uniformly distributed in the interval.
The implementation of the particle swarm-based photovoltaic cell multi-peak maximum power tracking method and system has the following beneficial effects:
1. the invention utilizes the global search characteristic of the particle swarm algorithm based on the microbial behavior mechanism to be applied to the aspect of maximum power tracking of the photovoltaic cell, avoids falling into local optimization, improves the search speed, reduces the oscillation and reduces the loss.
2. In the parameter setting of the particle swarm optimization, the traditional weight is kept unchanged, and the inertia weight parameter which is deepened to each generation of the particle swarm and each particle in each generation is optimized to be self-adaptively adjusted, so that the precision is improved, and the maximum power tracking is better realized.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a schematic block diagram of a circuit structure of photovoltaic maximum power tracking according to an embodiment of the present invention;
fig. 2 is a flowchart of a photovoltaic cell multi-peak particle swarm maximum power tracking control method according to an embodiment of the present invention;
fig. 3 is a circuit structure diagram of a photovoltaic cell multi-peak particle swarm maximum power tracking control method according to an embodiment of the invention;
fig. 4 is a simulation model of a photovoltaic cell multi-peak particle swarm maximum power tracking control method according to an embodiment of the invention;
FIG. 5 is a graph of the output power of a photovoltaic array controlled by the method of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
Referring to fig. 1, it is a schematic block diagram of a circuit structure of photovoltaic maximum power tracking of an embodiment of the present invention in fig. 1, where the circuit operating structure includes:
the photovoltaic cell array module, the DC/DC converter module and the load or inverter module are arranged in the power grid, wherein the control unit comprises a data acquisition unit, a particle swarm algorithm and a controller unit;
the data acquisition unit acquires the photovoltaic cell array to obtain an output current and an output voltage value required by the particle swarm algorithm, and the maximum power is calculated and determined by the particle swarm algorithm;
and the controller unit outputs the duty ratio obtained by the particle swarm to the DC/DC converter, stably works at the working voltage point obtained by the particle swarm algorithm and supplies electric energy to a load.
Referring to fig. 2, the multi-peak maximum power tracking method for photovoltaic cells based on particle swarm of the invention comprises:
s1, open circuit voltage U according to photovoltaic cell arrayocIn [0, U ]oc]M points are selected as the initial voltage V of the particle ii(k)，i∈[1,2,3,…,m]K represents the number of iterations, k is 0 at the initial voltage, and m is a positive integer greater than 1; initializing parameters of the particle population, including values of the particle weight omega and a self-learning factor c1Social learning factor c2The setting range of (1); in [0, Uoc]The requirement for the selection of the m points is a linear uniform selection or a uniform distribution of the particles at a theoretical value equal to 0.8Uoc. Learning factor c1And c2Is (0,2), learning factor c1And c2Randomly taking values in the interval of (0, 2).
S2, calculating the fitness value of each particle, wherein the fitness value is equal to the output power of the photovoltaic cell array; and searching the individual optimal position and the global optimal position of the population according to the fitness value of each particle.
The calculation formula of the fitness value is as follows:
Figure BDA0001951794510000061
Figure BDA0001951794510000062
the speed of the particle i in the k iteration represents the voltage value output by the photovoltaic cell;
Figure BDA0001951794510000063
representing the current measured by the current output voltage of the photovoltaic cell;
Figure BDA0001951794510000064
the objective function value of the particles represents the output power corresponding to the current output voltage of the photovoltaic cell, namely the fitness value.
And S3, updating the position and the speed of each particle to obtain a new fitness value of the next generation particle population.
In order to accelerate the convergence rate of the algorithm, a larger omega value is set in the initial stage, so that the algorithm is not easy to fall into local optimum; the value of the particle weight omega is a linear decreasing weight, and the calculation formula is as follows:
Figure BDA0001951794510000065
where c is the adjustment coefficient between 0 and 1, k is the number of current iterations, maxgen is the maximum number of iterations, ωminIs the minimum inertial weight, ωmaxAt maximum inertial weight, fii kIs the fitness value of the ith particle in the kth generation,
Figure BDA0001951794510000066
and
Figure BDA0001951794510000067
respectively the minimum and maximum fitness value of the ith particle in the kth generation,
Figure BDA0001951794510000068
is the weight of the ith particle in the kth generation.
The updating of the position and the velocity of each particle specifically includes:
searching in n-dimensionIn space, a population consisting of m particles is denoted as X ═ X (X)1,...,xi，...,xm) The position of the ith particle is xi＝(xi1，xi2，...xin)TVelocity vi＝(vi1，vi2，...vin)TThe self-optimum position P searched by the ith particle in the space search processi＝(pi1，pi2，...pid)TGlobal optimum position Pg＝(pg1，pg2，...pgd)TEach particle represents a solution, and the position and velocity of the particle are updated in an iterative manner, according to the formula:
Figure BDA0001951794510000069
Figure BDA00019517945100000610
is the velocity of particle i at the kth iteration, is
Figure BDA00019517945100000611
A velocity component of dimension d;
Figure BDA00019517945100000612
is the position of the particle i at the kth time,
Figure BDA00019517945100000613
is that
Figure BDA00019517945100000614
A position component of d-th dimension;
Figure BDA00019517945100000615
is the individual optimal position of the particle i at the kth iteration,
Figure BDA00019517945100000616
is that
Figure BDA00019517945100000617
An individual optimal position component of the d-th dimension;
Figure BDA00019517945100000618
is the global optimum position of the particle population at the kth iteration,
Figure BDA00019517945100000619
is that
Figure BDA00019517945100000620
The optimal position component in the d-th dimension,
Figure BDA0001951794510000071
is the weight of the ith particle in the kth generation, r1And r2Is at [0,1 ]]Random numbers are uniformly distributed in the interval.
S4, respectively comparing the individual optimal position and the global optimal position of the next generation particle population with the individual optimal position and the global optimal position before the update so as to respectively update the individual optimal position and the global optimal position, wherein the updating criterion is as follows: and after calculating the fitness value of the current position, comparing the fitness value with the optimal fitness value before updating, taking the position corresponding to the larger fitness value as a new individual optimal position of the particle, and taking the position corresponding to the maximum fitness value in all the new individual optimal position particles as a global optimal position. Namely: calculating the fitness value of the current position of each particle, and comparing the fitness value with the optimal fitness value experienced by each particle, wherein the best particle updates the position of the particle to pbesti(optimal for the ith particle); find the best fitness value among all the particles, update gbest(global optimum).
The flight speed of the particles consists of a momentum part, a cognitive part and a social part; the particle position is determined by the position of the last iteration plus the speed of the motion; the objective function f is a fitness function for measuring the quality of the position of the particle. The flying speed v of the particles is delta U, the v is in direct proportion to the distance of the maximum power point, the r1r2 ensures that the voltage step has randomness, and the larger probability approaches the maximum power point; the position x of the particle corresponds to the dc side Vdc; and (3) judging whether the photovoltaic cell assembly operates at the maximum power point by using a fitness function, namely a formula (2). In the invention, n is 1, namely a one-dimensional search space, the number m of population particles is set to be 10, the maximum iteration step number maxgen is 30, the output power is taken as a fitness value, the self-adaptive weight is added, and a termination condition is set, namely when the difference value between the power value of the maximum particles and the power value of the minimum particles is less than 0.04W, if the maximum step number is not iterated, the MPPT search is ended.
And S5, returning to the step S3 until a convergence condition is reached or the maximum iteration number is reached, and taking the final global optimal position as the working voltage corresponding to the maximum power point of the photovoltaic system. The convergence condition is reached by: and judging the difference value between the maximum and minimum fitness values corresponding to all the particles, if the difference value is smaller than a preset difference value, indicating that a convergence condition is reached, and finishing the execution of the particle swarm algorithm, otherwise, not reaching the convergence condition.
In the circuit of the maximum power tracking control method shown in fig. 3, a maximum power point controller (MPPT controller) obtains a current-voltage value obtained by measurement through an algorithm and the controller, and then obtains a duty ratio that can be output to a switching tube, and controls the switching tube to be turned on or off so that the system works at a desired voltage point. When the MPPT controller outputs high level and the switching tube T is switched on, the diode D is connected to the anode of the battery panel to bear back pressure and is cut off, the capacitor C supplies power to the load, the voltage of the battery panel is completely applied to two ends of the inductor L, the current of the inductor linearly increases, and the stored magnetic field energy gradually increases until the switching tube is cut off; when the MPPT controller outputs low level, when the switch tube T is cut off, because L's self characteristic, can change L's voltage polarity, the panel is established ties with the inductance like this and flows to the load through diode D, and the inductance current linearity reduces until switch tube T is switched on.
According to the flow chart of fig. 2 and the circuit diagram of fig. 3, the simulation model diagram of fig. 4 is constructed, wherein the S-Function part is the code implementation of the particle swarm algorithm.
The final output power of 122W can be obtained from the photovoltaic array output power curve diagram in fig. 4, the final photovoltaic cell output power is stabilized at the maximum power point, and the output stability and oscillation are small, so that the method can be basically obtained, the problem of multi-peak maximum power tracking can be effectively solved, the defect that the traditional method is trapped into local advantages is overcome, and the problem of maximum power tracking is realized.
In order to solve the technical problem, the invention provides a photovoltaic cell multi-peak maximum power tracking system based on particle swarm, which corresponds to the method, and can refer to the method specifically. The system comprises:
an initialization module for the open circuit voltage U according to the photovoltaic cell arrayocIn [0, U ]oc]M points are selected as the initial voltage V of the particle ii(k)，i∈[1,2,3,…,m]K represents the number of iterations, k is 0 at the initial voltage, and m is a positive integer greater than 1; initializing parameters of the particle population, including values of the particle weight omega and a self-learning factor c1Social learning factor c2The setting range of (1);
the optimal position calculation module is used for calculating the fitness value of each particle, and the fitness value is equal to the output power of the photovoltaic cell array; searching an individual optimal position and a global optimal position of the population according to the fitness value of each particle;
the particle population updating module is used for updating the position and the speed of each particle to obtain a new fitness value of the next generation particle population;
an optimal position updating module for respectively comparing the individual optimal position and the global optimal position of the next generation particle population with the individual optimal position and the global optimal position before the update so as to respectively update the individual optimal position and the global optimal position, and the updating criterion is as follows: after calculating the fitness value of the current position, comparing the fitness value with the optimal fitness value before updating, taking the position corresponding to the larger fitness value as a new individual optimal position of the particle, and taking the position corresponding to the maximum fitness value in all the new individual optimal position particles as a global optimal position;
and the optimal position updating module is used for returning to the particle population updating module until a convergence condition is reached or the maximum iteration number is reached, and taking the final global optimal position as the working voltage corresponding to the maximum power point of the photovoltaic system.
Further, in the particle swarm-based photovoltaic cell multi-peak maximum power tracking system of the present invention, the value of the particle weight ω is a linear decreasing weight, and the calculation formula is:
Figure BDA0001951794510000081
where c is the adjustment coefficient between 0 and 1, k is the number of current iterations, maxgen is the maximum number of iterations, ωminIs the minimum inertial weight, ωmaxIs the maximum inertial weight, fi kIs the fitness value of the ith particle in the kth generation,
Figure BDA0001951794510000082
and
Figure BDA0001951794510000083
respectively the minimum and maximum fitness value of the ith particle in the kth generation,
Figure BDA0001951794510000084
is the weight of the ith particle in the kth generation.
Further, in the particle swarm-based photovoltaic cell multi-peak maximum power tracking system of the present invention, the updating of the position and the speed of each particle specifically means:
in the n-dimensional search space, a population consisting of m particles is denoted as X ═ X (X)1,...,xi，...,xm) The position of the ith particle is xi＝(xi1，xi2，...xin)TVelocity vi＝(vi1，vi2，...vin)TThe self-optimum position P searched by the ith particle in the space search processi＝(pi1，pi2，...pid)TGlobal optimum position Pg＝(pg1，pg2，...pgd)TEach particle represents a solution, and the position and velocity of the particle are updated in an iterative manner, with the formula：
Figure BDA0001951794510000091
Figure BDA0001951794510000092
Is the velocity of particle i at the kth iteration, isA velocity component of dimension d;
Figure BDA0001951794510000094
is the position of the particle i at the kth time,
Figure BDA0001951794510000095
is that
Figure BDA0001951794510000096
A position component of d-th dimension;
Figure BDA0001951794510000097
is the individual optimal position of the particle i at the kth iteration,
Figure BDA0001951794510000098
is that
Figure BDA0001951794510000099
An individual optimal position component of the d-th dimension;
Figure BDA00019517945100000910
is the global optimum position of the particle population at the kth iteration,
Figure BDA00019517945100000911
is that
Figure BDA00019517945100000912
The optimal position component in the d-th dimension,
Figure BDA00019517945100000913
is the weight of the ith particle in the kth generation, r1And r2Is at [0,1 ]]Random numbers are uniformly distributed in the interval.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (8)

1. A multi-peak maximum power tracking method of a photovoltaic cell based on particle swarm is characterized by comprising the following steps:
s1, open circuit voltage U according to photovoltaic cell arrayocIn [0, U ]oc]M points are selected as the initial voltage V of the particle ii(k)，i∈[1,2,3,…,m]K represents the number of iterations, k is 0 at the initial voltage, and m is a positive integer greater than 1; initializing parameters of the particle population, including values of the particle weight omega and a self-learning factor c1Social learning factor c2The setting range of (1);
s2, calculating the fitness value of each particle, wherein the fitness value is equal to the output power of the photovoltaic cell array; searching an individual optimal position and a global optimal position of the population according to the fitness value of each particle;
s3, updating the position and the speed of each particle to obtain a new fitness value of the next generation particle population;
s4, respectively comparing the individual optimal position and the global optimal position of the next generation particle population with the individual optimal position and the global optimal position before the update so as to respectively update the individual optimal position and the global optimal position, wherein the updating criterion is as follows: after calculating the fitness value of the current position, comparing the fitness value with the optimal fitness value before updating, taking the position corresponding to the larger fitness value as a new individual optimal position of the particle, and taking the position corresponding to the maximum fitness value in all the new individual optimal position particles as a global optimal position;
s5, returning to the step S3 until a convergence condition is reached or the maximum iteration number is reached, and taking the final global optimal position as the working voltage corresponding to the maximum power point of the photovoltaic system;
the value of the particle weight omega is a linear decreasing weight, and the calculation formula is as follows:
Figure FDA0002372581070000011
where c is the adjustment coefficient between 0 and 1, k is the number of current iterations, maxgen is the maximum number of iterations, ωminIs the minimum inertial weight, ωmaxIn order to be the maximum inertial weight,
Figure FDA0002372581070000012
is the fitness value of the ith particle in the kth generation,
Figure FDA0002372581070000013
and
Figure FDA0002372581070000014
respectively the minimum and maximum fitness value of the ith particle in the kth generation,
Figure FDA0002372581070000015
is the weight of the ith particle in the kth generation.
2. The particle swarm-based photovoltaic cell multi-peak maximum power tracking method according to claim 1, wherein the peak power is [0, U ]oc]The requirement for the selection of the m points is a linear uniform selection or a uniform distribution of the particles at a theoretical value equal to 0.8Uoc。
3. The particle swarm-based photovoltaic cell multi-peak maximum power tracking method according to claim 1, wherein the learning factor c is1And c2Setting of (2)Range is (0,2), learning factor c1And c2Randomly taking values in the interval of (0, 2).
4. The particle swarm-based photovoltaic cell multi-peak maximum power tracking method according to claim 1, wherein the updating of the position and speed of each particle specifically means:
in the n-dimensional search space, a population consisting of m particles is denoted as X ═ X (X)1,...,xi，...,xm) The position of the ith particle is xi＝(xi1，xi2，...xin)TVelocity vi＝(vi1，vi2，...vin)TThe self-optimum position P searched by the ith particle in the space search processi＝(pi1，pi2，...pid)TGlobal optimum position Pg＝(pg1，pg2，...pgd)TEach particle represents a solution, and the position and velocity of the particle are updated in an iterative manner, according to the formula:
Figure FDA0002372581070000021
Figure FDA0002372581070000022
is the velocity of particle i at the kth iteration, is
Figure FDA0002372581070000023
A velocity component of dimension d;
Figure FDA0002372581070000024
is the position of the particle i at the kth time,
Figure FDA0002372581070000025
is that
Figure FDA0002372581070000026
A position component of d-th dimension;
Figure FDA0002372581070000027
is the individual optimal position of the particle i at the kth iteration,
Figure FDA0002372581070000028
is that
Figure FDA0002372581070000029
An individual optimal position component of the d-th dimension;
Figure FDA00023725810700000210
is the global optimum position of the particle population at the kth iteration,
Figure FDA00023725810700000211
is that
Figure FDA00023725810700000212
The optimal position component in the d-th dimension,
Figure FDA00023725810700000213
is the weight of the ith particle in the kth generation, r1And r2Is at [0,1 ]]Random numbers are uniformly distributed in the interval.
5. The particle swarm-based multi-peak maximum power tracking method for photovoltaic cells as recited in claim 1, wherein the reaching of the convergence condition is:
and judging the difference value between the maximum and minimum fitness values corresponding to all the particles, if the difference value is smaller than a preset difference value, indicating that a convergence condition is reached, and finishing the execution of the particle swarm algorithm, otherwise, not reaching the convergence condition.
6. The particle swarm-based photovoltaic cell multi-peak maximum power tracking method according to claim 1, wherein a calculation formula of the fitness value is as follows:
Figure FDA00023725810700000214
Figure FDA00023725810700000215
the speed of the particle i in the k iteration represents the voltage value output by the photovoltaic cell;
Figure FDA00023725810700000216
representing the current measured by the current output voltage of the photovoltaic cell;
Figure FDA00023725810700000217
the objective function value of the particles represents the output power corresponding to the current output voltage of the photovoltaic cell, namely the fitness value.
7. A photovoltaic cell multi-peak maximum power tracking system based on particle swarm is characterized by comprising:
an initialization module for the open circuit voltage U according to the photovoltaic cell arrayocIn [0, U ]oc]M points are selected as the initial voltage V of the particle ii(k)，i∈[1,2,3,…,m]K represents the number of iterations, k is 0 at the initial voltage, and m is a positive integer greater than 1; initializing parameters of the particle population, including values of the particle weight omega and a self-learning factor c1Social learning factor c2The setting range of (1);
the optimal position calculation module is used for calculating the fitness value of each particle, and the fitness value is equal to the output power of the photovoltaic cell array; searching an individual optimal position and a global optimal position of the population according to the fitness value of each particle;
the particle population updating module is used for updating the position and the speed of each particle to obtain a new fitness value of the next generation particle population;
an optimal position updating module for respectively comparing the individual optimal position and the global optimal position of the next generation particle population with the individual optimal position and the global optimal position before the update so as to respectively update the individual optimal position and the global optimal position, and the updating criterion is as follows: after calculating the fitness value of the current position, comparing the fitness value with the optimal fitness value before updating, taking the position corresponding to the larger fitness value as a new individual optimal position of the particle, and taking the position corresponding to the maximum fitness value in all the new individual optimal position particles as a global optimal position;
the optimal position updating module is used for returning to the particle population updating module until a convergence condition is reached or the maximum iteration number is reached, and taking the final global optimal position as a working voltage corresponding to the maximum power point of the photovoltaic system;
the value of the particle weight omega is a linear decreasing weight, and the calculation formula is as follows:
Figure FDA0002372581070000031
where c is the adjustment coefficient between 0 and 1, k is the number of current iterations, maxgen is the maximum number of iterations, ωminIs the minimum inertial weight, ωmaxIn order to be the maximum inertial weight,
Figure FDA0002372581070000032
is the fitness value of the ith particle in the kth generation,
Figure FDA0002372581070000033
and
Figure FDA0002372581070000034
respectively the minimum and maximum fitness value of the ith particle in the kth generation,
Figure FDA0002372581070000035
is the weight of the ith particle in the kth generation.
8. The particle swarm-based photovoltaic cell multi-peak maximum power tracking system of claim 7, wherein the updating of the position and speed of each particle specifically refers to:
in the n-dimensional search space, a population consisting of m particles is denoted as X ═ X (X)1,...,xi，...,xm) The position of the ith particle is xi＝(xi1，xi2，...xin)TVelocity vi＝(vi1，vi2，...vin)TThe self-optimum position P searched by the ith particle in the space search processi＝(pi1，pi2，...pid)TGlobal optimum position Pg＝(pg1，pg2，...pgd)TEach particle represents a solution, and the position and velocity of the particle are updated in an iterative manner, according to the formula:
Figure FDA0002372581070000036
Figure FDA0002372581070000037
is the velocity of particle i at the kth iteration, is
Figure FDA0002372581070000038
A velocity component of dimension d;
Figure FDA0002372581070000039
is the position of the particle i at the kth time,
Figure FDA00023725810700000310
is that
Figure FDA00023725810700000311
A position component of d-th dimension;
Figure FDA00023725810700000312
is the individual optimal position of the particle i at the kth iteration,
Figure FDA00023725810700000313
is that
Figure FDA00023725810700000314
An individual optimal position component of the d-th dimension;
Figure FDA00023725810700000315
is the global optimum position of the particle population at the kth iteration,
Figure FDA00023725810700000316
is that
Figure FDA00023725810700000317
The optimal position component in the d-th dimension,
Figure FDA00023725810700000318
is the weight of the ith particle in the kth generation, r1And r2Is at [0,1 ]]Random numbers are uniformly distributed in the interval.
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